Clinical Impact of Double Protease Inhibitor Boosting with Lopinavir/Ritonavir and Amprenavir as Part of Salvage Antiretroviral Therapy
Bibliographic record
Abstract
PURPOSE: Double protease inhibitor (PI) boosting is being explored as a new strategy in salvage antiretroviral (ARV) therapy. However, if a negative drug interaction leads to decreased drug levels of either or both PIs, double PI boosting could lead to decreased virologic response. A negative drug interaction has been described between amprenavir (APV) and lopinavir/ritonavir (LPV/r). This observational cohort study assessed the virologic impact of the addition of APV to a salvage ARV regimen, which also contains LPV/r, compared to a regimen containing LPV/r alone. METHOD: Patients initiated on a salvage ARV regimen that included LPV/r obtained from the expanded access program in Toronto, Canada, were evaluated. APV (600-1,200 mg bid) was added at the discretion of the treating physician. RESULTS: Using multivariate Cox proportional hazards models, we found that the addition of APV to a LPV/r-containing salvage regimen was not significantly associated with time to virologic suppression (< 50 copies/mL; adjusted hazard ratio [HR] = 0.75, p =.12) or with time to virologic rebound (adjusted HR = 1.46, p =.34). Those patients who received higher doses of APV had an increased chance of virologic suppression (p =.03). In a subset of 27 patients, the median LPV C(trough) was significantly lower in patients receiving APV (p =.04), and the median APV C(trough) was reduced compared to reported controls. CONCLUSION: Our data do not support an additional benefit in virologic reduction of double boosting with APV and LPV/r relative to LPV/r alone in salvage ARV therapy. Our study's limitations include its retrospective nature and the imbalance between the two groups potentially confounding the results. Although these factors were adjusted for in the multivariate analysis, a prospective randomized controlled trial is warranted to confirm our findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".